Labeling a significant location based on contextual data

ABSTRACT

Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.16/989,757, filed Aug. 10, 2020, entitled “LABELING A SIGNIFICANTLOCATION BASED ON CONTEXTUAL DATA,” which is a Divisional of U.S. patentapplication Ser. No. 15/272,282, “LABELING A SIGNIFICANT LOCATION BASEDON CONTEXTUAL DATA,” filed Sep. 21, 2016, now U.S. Pat. No. 10,739,159,which claims the benefit of priority of U.S. Provisional Application No.62/348,586, entitled “LABELING A SIGNIFICANT LOCATION BASED ONCONTEXTUAL DATA,” filed Jun. 10, 2016, each of which is hereinincorporated by reference.

TECHNICAL FIELD

This disclosure relates generally to location-based services.

BACKGROUND

A map service can provide maps to one or more computing devices. Themaps can include points of interest (POIs). A POI can be a place that isdesignated as useful or of interest to any user or all users. Forexample, a POI can be a shop, restaurant, or a hotel. Each POI may havean address (for example, a street address). To display a POI in a map,the map service can geocode the POI, including associating a location(for example, latitude and longitude coordinates) of the POI. The onlinemap service can display a marker representing the POI on the map at thelatitude and longitude coordinates. The inverse technique, reversegeocoding, attempts to determine a POI to which a given latitude andlongitude refers.

SUMMARY

This disclosure describes technologies relating to labeling asignificant location based on contextual data.

Certain aspects of the subject matter described in this disclosure canbe implemented as a method. A computing device determines a location ofthe computing device. The computing device determines a label for thedetermined location based on contextual data associated with thelocation.

This, and other aspects, can include one or more of the followingfeatures. The location can be a significant location that represents anentity that is estimated to have meaning to a user of the computingdevice. To determine the significant location of the computing device,the computing device can obtain location information associated with thecomputing device. The computing device can receive multiple points ofinterest associated with the location information. Each point ofinterest represents an entity at a geographic location near a geographiclocation of the computing device. The geographic location includes thelocation information. To determine the label for the determinedsignificant location based on contextual data associated with thesignificant location, the computing device can determine that one of themultiple points of interest is the determined significant location, anddetermine a label associated with the one of the multiple points ofinterest as the label for the determined significant location. Thecomputing device can obtain the label associated with the determinedsignificant location from an application executed by the computingdevice. The application can display the obtained label associated withthe determined significant location at a user interface location on auser interface displayed by the computing device. The user interfacelocation can correspond to a geographic location of the determinedsignificant location. The location information can include a geographicarea surrounding a geographic location of the computing device. Thegeographic area can represent an uncertainty associated with thegeographic location of the computing device. At least some of themultiple points of interest can be located within the geographic area.The geographic area can be a first geographic area. All of the multiplepoints of interest can be located within a second geographic area thatis larger than the first geographic area. The second geographic area canbe twice as large as the first geographic area. To determine that one ofthe multiple points of interest is the significant location, thecomputing device can determine that a likelihood that the one of themultiple points of interest is the significant location is greater thana likelihood that any of the remaining points of interest is thesignificant location. Multiple map unique identifiers can be stored.Each map unique identifier can uniquely identify a significant locationof multiple significant locations and include a respective label uniqueto each significant location of the multiple significant locations. Todetermine the label for the determined significant location based oncontextual data associated with the significant location, the computingdevice can identify a map unique identifier associated with thedetermined significant location. The contextual data can include apayment transaction using a payment application implemented by thecomputing device between the user and the determined significantlocation. The payment transaction can provide a time of the paymenttransaction and a significant location at which the payment transactionoccurred. To determine the label for the determined significant locationbased on contextual data associated with the significant location, thecomputing device can determine that the computing device was at thesignificant location at the time of the payment transaction. Thecontextual data can include a calendar event stored in a calendarapplication implemented by the computing device. The calendar event canmention the label of the significant location. To determine the labelfor the determined significant location based on contextual dataassociated with the significant location, the computing device canidentify the label of the significant location mentioned in the calendarevent. To identify the label of the significant location mentioned inthe calendar event, the computing device can determine that the calendarevent mentions a location, and determine that the calendar eventmentions a location. To identify the label of the significant locationincluded in the calendar event, the computing device can determine thatthe computing device is located at the significant location between timeboundaries specified in the calendar event. The contextual data caninclude a reminder stored in a calendar application implemented by thecomputing device. The reminder can mention the label of the significantlocation. To determine the label for the determined significant locationbased on contextual data associated with the significant location, thecomputing device can identify the label of the significant locationmentioned in the reminder. To identify the label of the significantlocation mentioned in the reminder, the computing device can determinethat the reminder mentions a location, and determine that the mentionedlocation is a significant location. The contextual data can includeinformation received from a map application implemented by the computingdevice. The information can include route information including adestination location. To determine the label for the determinedsignificant location based on contextual data associated with thesignificant location, the computing device can determine that thedestination location is the determined significant location. Todetermine that the destination location is the determined significantlocation, the computing device can determine a first time at which thedestination location was reached, a second time at which the computingdevice was located at or near the significant location, and that thedestination location is the determined significant location based ondetermining that the first time substantially matches the second time.The information can include destination locations identified as favoritelocations by the user of the computing device. To determine the labelfor the determined significant location based on contextual dataassociated with the significant location, the computing device candetermine that one of the locations identified as a favorite location bythe user is the determined significant location. The information caninclude destination locations previously viewed using the mapapplication by the user of the computing device. To determine the labelfor the determined significant location based on contextual dataassociated with the significant location, the computing device candetermine that one of the locations previously viewed using the mapapplication by the user is the determined significant location. Theinformation can include destination locations viewed using a third partyapplication implemented by the computing device. The map location canreceive a destination location viewed using the third party application.To determine the label for the determined significant location based oncontextual data associated with the significant location, the computingdevice can determine that the destination location viewed using thethird party application is the determined significant location. Thecontextual data can include communications between the computing deviceand at least one other computing device. The communications can includetext messages exchanged between the computing device and the at leastone other computing device. At least one of the text messages canmention the significant location. To determine the label for thedetermined significant location based on contextual data associated withthe significant location, the computing device can identify thesignificant location mentioned in the at least one of the text messages.The communications can include audio messages exchanged between thecomputing device and the at least one other computing device. At leastone of the audio messages can mention the significant location. Todetermine the label for the determined significant location based oncontextual data associated with the significant location, the computingdevice can identify the significant location mentioned in the at leastone of the audio messages. The audio messages can include voicemail. Atleast one of the audio messages can be included in an audio conversationbetween the computing device and the at least one other computingdevice. At least one of the audio messages can be included in a videoconversation between the computing device and the at least one othercomputing device. The communications can include at least one of a timeat which the computing device will be located at the significantlocation or a context based on which the significant location isidentifiable. The contextual data can include a movement pattern of thecomputing device between the multiple significant locations. Todetermine the label for the determined significant location based oncontextual data associated with the significant location, the computingdevice can determine that the computing device has a dwell time at thedetermined significant location that is greater than a threshold dwelltime. Based on the movement pattern, the computing device can determinea physical activity performed by the user of the computing device at thesignificant location. To determine the label for the determinedsignificant location based on contextual data associated with thesignificant location, the computing device can determine the label basedon the physical activity. The contextual data can include multiplecontextual data points. To determine the label for the determinedsignificant location based on contextual data associated with thesignificant location, the computing device can determine a potentialsignificant location based on each of the multiple contextual datapoints resulting in multiple potential significant locations, anddetermine one of the multiple potential significant locations to be thedetermined significant location. To determine one of the multiplepotential significant locations to be the determined significantlocation, the computing device can determine that more than half of themultiple potential significant locations are the same potentialsignificant locations. To determine one of the multiple potentialsignificant locations to be the determined significant location, thecomputing device can associate a respective weight to each potentialsignificant location, the respective weight based on the contextual datapoint based on which each potential significant location was determined.

Certain aspects of the subject matter described here can be implementedas a computer-readable medium storing instructions executable by one ormore processors to perform operations described here. Certain aspects ofthe subject matter described here can be implemented as a systemincluding one or more processors and a computer-readable medium storinginstructions executable by the one or more processors to performoperations described here

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an example virtual map presented by acomputing device showing POIs and a significant location.

FIG. 2A is a schematic diagram illustrating multiple points of interestnear a significant location.

FIG. 2B is a block diagram illustrating components of an example systemfor implementing labeling of significant locations based on contextualdata.

FIG. 3A is a block diagram illustrating contextual data sources used toidentify labels for significant locations.

FIG. 3B is a schematic diagram illustrating two geographic areasassociated with a significant location encompassing POIs.

FIG. 4 is a flowchart of an example process for identifying labels forsignificant locations.

FIG. 5 is a block diagram of an example device architecture forimplementing the features and processes described in reference to FIGS.1-4 .

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION Exemplary Use Cases

FIG. 1 is a schematic diagram of an example map presented on a displayof a computing device. In the example shown, the computing device (e.g.,a smartphone, tablet computer, wearable device, desktop computer orother computing device) executes a map application that displays a mapof a geographic area including three streets: Main Street (goingnorth-south), and cross streets First Street and Second Street, eachgoing east-west. The map application overlays on the map POI markers123, 124 representing a “MOVIE THEATER” and a “BURGER PLACE,”respectively. In general, a POI can be a business, landmark, publicpark, school or any other entity that is of interest to or is meaningfulto any user or all users.

In addition to POI markers 123, 124, the map application also displays amarker 122 representing a location of the computing device. In someinstances, the location of the computing device can be any geographiclocation at which the computing device is physically located. In anexample in which the computing device is a smartphone or a wearabledevice (for example, a smartwatch) or an automobile information andentertainment center, the marker 122 can be displayed at a location of auser possessing the smartphone. In some instances, the location of thecomputing device can be a location that is significant to the user ofthe computing device. For example, a significant location can be theuser's home or work address or a location that the user has visitedseveral times in the past (e.g., a frequently visited business).

Determining that a location is a significant location can includedetermining that the computing device dwells at the significant locationfor at least a threshold amount of time. As described below, thesignificant location can be inferred from clusters of latitude andlongitude readings from location sensors, e.g. GPS, which were gatheredpreviously. As such, the significant location may have associatedgeographic uncertainty—that is, its boundaries may not be preciselyknown. The associations between its cluster of latitude and longitudereadings and one or more POIs may also not be known. Determining that alocation is a significant location can include determining that thecomputing device dwells at the significant location for at least athreshold amount of time.

Therefore, a significant location may be represented by a marker on themap with a label that is not meaningful to the user. For example, asignificant location for the user may be a frequently visitedrestaurant, such as BURGER PLACE 124. The user would expect to see thesignificant location marker 122 located at, for example, the entrance toBURGER PLACE 124 with an appropriate label describing the restaurant.However, since significant locations are estimated (e.g., using aclassifier or machine learning), the location marker 122 is in themiddle of Main Street with a generic label (e.g., only a street address)that has no meaning to the user. This results in a poor user experiencewith the map application. By labeling the geographic location 122 with alabel that is meaningful to the user (e.g., “BURGER PLACE”), the mapapplication personalizes the significant location to the user, therebyenhancing the user's experience with the map application.

Implementations of the subject matter described in this disclosure canenhance operation of a computing device by using contextual data storedon or accessible by the computing device to improve the usefulness ofsignificant locations to a user. Contextual data can include data uniqueto the computing device or personalized for a user of the computingdevice (or both). The contextual data can include data collected basedon the user's usage of the computing device, for example, based onexecuting, using the computing device, one or more applications that thecomputing device is configured to execute. In some implementations, thecontextual data collected based on usage of the computing device caninclude any input provided to the computing device to execute anyapplication. The input can be expressly provided by the user or can beinherently obtained in response to the user performing an action. Thecontextual data can include any output provided by the computing deviceeither in response to any input or in response to executing anyapplication. The contextual data can include any information derived orinferred by the computing device based on usage (present or past usageor both) of the computing device, on an environment in which thecomputing device is or was present, based on information received fromthe computing device from a source other than the user (for example, acentral server, a WiFi service provider, a telephony service provider orother source) or any combination of them. For example, by leveragingcontextual data that is already being collected for other applicationsrunning on the computing device to label significant locations, theutility of significant locations to the user is improved withoutexpending additional resources to collect or derive additional data.This results in an improvement to the computing device by preservingmemory and storage and reducing power consumption by the computingdevice by not performing additional data collection and processing.

Exemplary Labeling of Significant Locations Based on Contextual Data

FIG. 2A is a schematic diagram illustrating POIs near a significantlocation 270. The schematic diagram illustrates that multiple POIs canfall within an uncertainty associated with a significant location. Inthe example shown, the significant location 270 is located among 8 POIs(POI 1-POI 8). The uncertainty associated with the significant location270 defines a geographic area of uncertainty 272 that encompasses asubset of the POIs (for example, POIs 2, 3, 4, 5, 6 and 7). In someimplementations, the computing device 200 can store or derive contextualdata associated with at least one of the subset of the POIs encompassedby the geographic area of uncertainty 272. For example, the user of thecomputing device 200 may have executed a payment transaction using thecomputing device 200 at POI 3. Contextual details associated with thepayment transaction (e.g., timestamp, merchant name and address or otherbusiness identifier) can be stored on the computing device 200 duringthe transaction. The contextual details associated with paymenttransactions can further show that the user has not executed a paymenttransaction at any of the other POIs in the subset of POIs over aspecified time period.

In another example, a disambiguation based on crowd-sourced paymenthistory can be used to identify POI 3. For example, several users ofrespective computing devices may have executed respective paymenttransactions using their respective computing devices at POI 3.Contextual details associated with each payment transaction (e.g.,timestamp, merchant name and address or other business identifier) canbe stored on respective computing devices during respectivetransactions. The contextual details associated with paymenttransactions can further show that some or all the users have notexecuted payment transactions at any of the other POIs in the subset ofPOIs over a specified time period.

In a further example, the user of the computing device 200 may havefrequented POI 3 multiple times over a duration (for example, a week, amonth). The computing device 200 can store a dwell time of the computingdevice 200 at POI 3 or a map route to the POI 3. The number of visitscan be tracked with a software counter. The dwell time can be determinedfrom a motion sensor (e.g., accelerometer) or GNSS receiver on thecomputing device 200 and a timer. Based on such contextual details (orother contextual details such as those described below), the computingdevice 200 can determine that, out of all the POIs in the subset, POI 3is most likely to be the significant location. For example, based onmultiple visits to the same geographic location and multiple paymenttransactions at POI 3 during each of those visits, the computing device200 can determine that POI 3 is most likely to be the significantlocation. The maps engine 206 can identify a label (e.g., a merchantname) associated with POI 3. For example, the label can be stored in thePOI database 210. The identified label is meaningful to the user of thecomputing device 200 because the user has visited POI 3 one or moretimes in the past.

FIG. 2B is a block diagram illustrating components of an exemplarycomputing device 200 implementing labeling of locations, for example,significant locations or any location, based on contextual dataassociated with or stored on (or both) the computing device. Returningto the example described above with reference to FIG. 2A, byimplementing the techniques described below with reference to FIG. 2B,the location of the computing device 200 can be resolved to POI 3 fromamong the subset of POIs, namely, POIs 2, 3, 4, 5, 6 and 7. Thecomputing device 200 can include one or more processors and acomputer-readable storage medium storing instructions executable by theone or more processors to perform operations described herein.Additional details about the computing device 200 architecture aredescribed below with reference to FIG. 5 . The computing device 200includes multiple components (for example, multiple modules), each ofwhich can be implemented as computer instructions executable by the oneor more processors, as described in reference to FIG. 5 .

In some implementations, the computing device 200 includes a significantlocation estimator 202. The significant location estimator 202 canoutput significant locations 204. Each significant location can beassociated with an entity POI that is located at a geographic location.Each POI is estimated to have meaning to any user of any computingdevice or all users of respective computing devices. For example, asignificant location can include the user's home or work place or otherlocation that the user of the computing device frequently visits. Forexample, a significant location can include a business such as theuser's favorite restaurants, movie theaters, or other shops.

In some implementations, the computing device 200 includes a locationsource 203 that can output locations 205. Each location output by thelocation source 203 can be associated with a POI that is located at ageographic location. A location output by the location source 203 may ormay not have significance to the user of the computing device.Therefore, a location output by the location source 203 may overlap witha location output by the significant location estimator 202.

In some implementations, each location (e.g., location output by thesignificant location estimator 202 or the location source 203) can berepresented by an estimated geographic location and data representing ameasure of uncertainty or error in the estimated location. Thegeographic location can be identified by position coordinates in anyreference coordinate frame such as a geodetic or East North Up (ENU)reference coordinate frame. In an implementation, the geographiclocation is represented by latitude, longitude and altitude. Theuncertainty or error can be a statistical variance associated with theestimated significant location. In an implementation, the error can berepresented on a map as a geographic area (e.g., a circle or otherpolygon) surrounding the estimated significant location. The exactlocation of the significant location can be anywhere within thegeographic area. In some implementations, the significant locationestimator 202 and the location source 203 can generate, output and storea list of significant locations 204 and a list of locations 205,respectively. Over time, the significant location estimator 202 canupdate the list, for example, by adding more significant locations orremoving locations that are no longer significant (e.g., locations thathave not been visited for a long period of time). In an implementation,the list is an ordered list with the significant location at the top ofthe list having the lowest uncertainty or error. Similarly, over time,the locations in the location source 203 can also be updated.

The computing device 200 can include a maps engine 206 connected to amaps database 208 and a POI database 210. The maps engine 206 is alsoaccessible by computing device applications 218 a-218 d through, forexample an application programming interface (API). The computing deviceapplications 218 a-218 d can request map data from maps engine 206 thatcan be rendered into a map. In an implementation, the maps database 208and POI database 210 can be a single database. The maps database 208maintains map data for generating a map including data for renderingstreets, highways, freeways and the like on the map. The POI database210 maintains a list of entities (e.g., businesses, public landmarks,parks, schools, hospitals) that can be represented by markers (e.g.,virtual pushpins) overlaid on the map. For example, the maps database208 can store a street address of an entity and the POI database 210 canstore information identifying the entity as a particular restaurant,which can serve as the label for the entity. In particular, POI database210 can store one or more labels for each of the entities. The one ormore labels can include, for example, a business name of an entityprovided by a POI service, a tag entered by a user for the entity, or acontact name associated with the entity in a contact list (e.g., a homeor work address). The maps database 208 and the POI database 210 canprovide the list of entities and their identities to the maps engine206. The maps engine 206 can display markers representing thesignificant locations in the map together with the markers for the POIs.

The computing device 200 includes a context analyzer 212 configured tocollect and analyze contextual data to identify and provide a label to alocation (e.g., a location output by the significant location estimator202 or a location output by the location source 203) at or near whichthe computing device is located. In some implementations, a locationprocess in the computing device 200 can obtain (e.g., receive orestimate) a geographic location of the computing device 200. Forexample, the location processor can receive or derive the geographiclocation of the computing device from signals transmitted by aterrestrial-based or satellite-based location estimation system, suchas, for example, a WiFi station, cell tower or Global NavigationSatellite System (GNSS). An example GNSS system is the GlobalPositioning System (GPS). The geographic location of the computingdevice can be represented by a latitude, longitude and altitude. Theestimated geographic location of the computing device can also includean uncertainty, which can be represented by a geographic area (e.g.,circular region or other polygon) surrounding the estimated geographiclocation of the computing device. In some implementations, the contextanalyzer 212 can generate and output a list of significant locations 202that includes, for each significant location, location information (forexample, latitude or longitude or altitude or any combination of them,and an uncertainty) and a maps unique identifier (MUID) (describedbelow).

The maps engine 206 can obtain POIs associated with, for example, near,a significant location. As described earlier, each POI represents anentity within or near a geographic area of uncertainty surrounding theestimated significant location. For example, the maps engine 206 canprovide the POIs, which are stored in the POI database 210 or retrievedfrom a network-based POI service if available. The geographic locationsof the POIs can reside inside or outside the geographic area ofuncertainty associated with the estimated significant location. The mapsengine 206 can determine that at least one of the POIs is a significantlocation by comparing the geographic locations of the POIs with thegeographic locations of the significant locations in the list ofsignificant locations 204 provided by the significant location estimator202. If a significant location is the same as a POI location, then thesignificant location can be replaced by the POI or can assume the labelof the matching POI. If, however, there is no match, the contextanalyzer 212 can determine that one of the POIs is a significantlocation based on contextual data associated with the POIs, as describedwith reference to FIG. 2B.

FIG. 3A is a block diagram illustrating contextual data sources 214 usedto identify labels for significant locations. The contextual datasources 214 can include multiple components, each storing contextualdata using which the context analyzer 212 can identify a labelassociated with a significant location. Several examples of contextualdata for identifying labels associated with significant locations aredescribed below. If the label is associated with a POI in the POIdatabase 210, the POI can be associated with a POI identifier or a mapunique identifier (MUID). When a computing device application 218 a-218d running on the computing device 200 requests a significant locationthat can be mapped to a nearby POI, the MUID for the POI can be providedto the requesting application. The requesting application can then sendthe MUID to the maps engine 206 (e.g., through an API), which can beused by the maps to retrieve the label from the POI database 210.

In the examples for identifying a label described below, an identifiedlabel is described as being displayed in a user interface. In someimplementations, a confidence level for the identified label can bedetermined and displayed in the user interface together with the label.The confidence level can represent a likelihood that the identifiedlabel matches the accurate label of the significant location. Forexample, the confidence level can be indicated by a marker (for example,by presenting a question mark next to the label or by presenting thelabel in a particular color or other marker). The marker is editableallowing a user to either confirm that the identified label is accurateor to correct the label. A correction to a label can include re-labelingthe displayed label to be the accurate label. Alternatively or inaddition, the correction can include labeling a different, more accuratesignificant location with the identified label. In response to the userediting the label, the edit is reflected on other appearances of thesame significant location and when the computing device is located atthe significant location at a future instance.

Identifying a Label Based on Payment Transaction

In some implementations, computing device 200 can determine a label fora significant location based on payment transaction data 302 or ahistory of payment transactions stored by a payment applicationimplemented by the computing device 200. The payment transactions caninclude a time of the payment transaction, the location of thetransaction (e.g., point of sale) and an identity (e.g., merchant nameor business identifier) of the significant location at which the paymenttransaction occurred. In some cases, the significant location can be aPOI in the POI database 210. For example, if the user is at a restaurant(labeled “Burger Place”) and executes the payment application on thecomputing device 200 to complete a transaction with the restaurant, thenthe payment application can store a time of the transaction, thelocation of the transaction and the merchant name “Burger Place” aspayment transaction data 302. When the significant location estimator202 provides a significant location, the significant location can beassociated with the MUID for “Burger Place” based on the transactiondata. A marker for the significant location is displayed on the map withthe label “Burger Place.” The label “Burger Place” has meaning to theuser because the user purchased food at the Burger Place one or moretimes in the past.

Identifying a Label Based on Calendar Events

In some implementations, computing device 200 can determine a label fora significant location based on calendar event data 304. The contextualdata sources 214 can include calendar event data 304 received from acalendar application implemented by the computing device 200. Thecalendar event data 304 can include a location field with an address ora text string that includes a “hint” of a location. For example, thetext string could recite “Dinner at Bob's Summer home in the Hamptons.”That text can be used to search a contact or address book for Bob'sSummer home address. A label of a significant location that is nearBob's Summer home can be updated with the exact address found in thecontact or address book. If there is a populated location field, theaddress can be taken from the calendar event data 304.

Identifying a Label Based on Reminders

In some implementations, computing device 200 can determine a label fora significant location based on reminders data 306. The contextual datasources 214 can include reminders data 306 received from one or moreapplications implemented by the computing device 200. For example, thereminders data 306 can be received from the calendar application.Alternatively, the reminders data 306 can be received from a notesapplication executed by the computing device 200 to create and store anote in reply to instructions from the user. For example, in response tothe user speaking a voice command, “Remind me to get milk at the grocerystore,” the computing device 200 can create a reminder with the textspoken by the user. In this example, the reminder mentions thesignificant location, namely, “the grocery store”. The context analyzer212 can determine that “grocery store” represents a significantlocation. For example, the context analyzer 212 can determine if anyPOIs near the significant location is a grocery store and use the POIlabel for the grocery store to label the significant location with ameaningful label. In some cases, there may be multiple grocery storePOIs near the significant location. In these cases, additionalcontextual data sources 214 can be used to narrow the multiple POIs to asingle POI. For example, if the user has visited a particular POI morethan others in the past, that contextual data can be used to filter thePOIs to a single POI. In another example, transaction data can be usedto filter the POIs as described above.

Identifying a Label Based on Map Data

In some implementations, computing device 200 can determine a label fora significant location based on map data. The contextual data sources214 can include map data 308 received from a map application implementedby the computing device 200, for example, the maps engine 206.

For example, the map data 308 can include route data 308 a that storesroutes traveled by the computing device 200. The route data 308 a caninclude route information including a destination location. Thedestination location can be a location that the significant locationestimator 202 has previously determined to be a significant location.The context analyzer 212 can determine that the destination location isat or near a POI with an assigned label. Based on this determination,the context analyzer 212 can assign the label of the POI to thesignificant location and store the label in the route data 308 a. Inthis manner, the context analyzer 212 can use the route data 308 a tolabel the significant location.

In another example, the map data 308 can include favorites data 308 bthat stores locations identified as “Favorites” by the user of thecomputing device 200. The locations identified as favorites can besignificant locations. One or more of the significant locations, e.g.,locations identified as favorites, can also be POIs with respectiveassigned labels. The context analyzer 212 can determine that asignificant location identified as a favorite is also a POI. Based onthis determination, the context analyzer 212 can assign the label of thePOI to the significant location and store the label in the favoritesdata 308 b.

In a further example, the map data 308 can include history data 308 cthat stores locations that the user of the computing device 200 viewedusing the maps application or that the user viewed and visited. Forexample, the user can view a significant location in the mapsapplication by selecting a marker on a map representing the significantlocation to view details and perhaps photos of the significant location.A location that the user viewed and visited has a higher likelihood ofbeing a significant location than a location that a user only viewed,especially when the user visited the location multiple times. One ormore of the significant locations, i.e., locations that the user viewed(or viewed and visited), can also be POIs with respective assignedlabels. The context analyzer 212 can determine that a significantlocation that the user viewed (or viewed and visited) is also a POI.Based on this determination, the context analyzer 212 can assign thelabel of the POI to the significant location and store the label in thehistory data 308 c.

In another example, the map data 308 can include third party data 308 d.The computing device 200 can be configured to implement third partyapplications, i.e., applications created by parties other than thosethat developed the computing device 200. Certain third partyapplications can provide locations to the user of the computing device200, for example, in response to a search request. An exemplary thirdparty application is one that provides suggestions for restaurants inresponse to a user searching for restaurants within a geographic area.The maps application can display the geographic location of a restaurantprovided by such a third party application. In addition, the mapsapplication can store location information associated with therestaurant including, for example, the geographic location, therestaurant's name or other location information. The computing device200 can determine that the restaurant provided by the third partyapplication is a significant location, for example, if the user visitsthe restaurant (one or more times) or if the user frequently views therestaurant using the third party application (or both). In this manner,the computing device 200 can identify multiple significant locationsbased on usage of the third party application. One or more of thesignificant locations, i.e., locations that the user viewed (or viewedand visited) using the third party application, can also be POIs withrespective assigned labels. The context analyzer 212 can determine thata significant location that the user viewed (or viewed and visited)using the third party application is also a POI. Based on thisdetermination, the context analyzer 212 can assign the label of the POIto the significant location and store the label in the third party data308 d.

In a further example, accessing a POI on a third party application (forexample, viewing, browsing or selecting the POI) is, by itself, anindication that the POI is relevant. That is, the POI is relevant evenif the map data 308 does not receive the POI from the third partyapplication. In such instances, the POI selected using the third partyapplication can be stored (for example, as “favorite” or “relevant”locations) and be matched against potential POIs at which the computingdevice 200 can be located.

Identifying a Label Based on Communications Data

In some implementations, computing device 200 can determine a label fora significant location based on communications data. The contextual datasources 214 can include communications data 310 received from acommunication application implemented by the computing device 200. Forexample, the communications data can include communications between thecomputing device 200 and at least one other computing device. Thecommunication application can be a telephone application, a text messageapplication, an audio communication application, an email application, adigital assistant application, a video communication application or anyother application in which communications are sent from or received bythe computing device 200.

For example, the communications data 310 can include text data 310 athat stores text messages exchanged between the computing device 200 andat least one other computing device. The text messages can include textthat mentions a significant location. The location mentioned in the textmessage can also be a POI with an assigned label. The context analyzer212 can extract text from the text message and identify a POI (forexample, a business) whose name is the same as or substantially similarto the extracted text. Upon finding a match, i.e., upon determining thata threshold likelihood that the extracted text matches the business nameis satisfied, the context analyzer 212 can assign the label of the POIto the significant location and store the label in the text data 310 a.

In another example, the communications data 310 can include audio data310 b that stores audio messages exchanged between the computing device200 and at least one other computing device. The audio messages caninclude audio exchanged during voice calls, audio included in voicemailmessages (either incoming voicemail message or voicemail messages lefton other devices using the computing device 200), audio included inother communication applications or combinations of them. The audiomessages can include audio that mentions a significant location. Thelocation mentioned in the audio message can also be a POI with anassigned label. The context analyzer 212 can identify a POI (forexample, a business) whose name is the same as or substantially similarto that of a significant location mentioned in the audio message. Uponfinding a match, i.e., upon determining that a threshold likelihood thatthe name in the audio message matches the business name is satisfied,the context analyzer 212 can assign the label of the POI to thesignificant location and store the label in the audio data 310 b.

In a further example, the communications data 310 can include video data310 c that stores video messages (including images, such as video, andaudio) exchanged between the computing device 200 and at least one othercomputing device. The video messages can include images or audio (orboth) that mentions a significant location. The location mentioned inthe video message can also be a POI with an assigned label. The contextanalyzer 212 can identify a POI (for example, a business) whose name isthe same as or substantially similar to that of a significant locationmentioned in the video message. Upon finding a match, i.e., upondetermining that a threshold likelihood that the name in the videomessage matches the business name is satisfied, the context analyzer 212can assign the label of the POI to the significant location and storethe label in the audio data 310 b.

In some implementations, the communication data can include a mention ofa time or a context based, either or both of which can be used toresolve multiple POIs to a single significant location. For example, thecommunication data can include the statement “See you at the Burger Kingaround 8 pm” in any format (i.e., audio, video, text or combinations ofthem). The mention of time—“8 pm”—in the communication can be used tofurther resolve the significant location at which the computing deviceis present. In another example, the communication data can include thestatement “I will eat sushi tonight at 8 pm.” The mention of “sushi” canprovide a context indicating that the computing device will be locatedat a Japanese restaurant at 8 pm. The context, taken alone or incombination with the time, can be used to resolve the significantlocation at which the computing device is present.

Identifying a Label Based on Movement Data

In some implementations, computing device 200 can determine a label fora significant location based on movement data. The contextual datasources 214 can include movement data 312 received from an inertialsensor, GNSS receiver or digital pedometer application or other movementdata source available to the computing device 200. For example, thecomputing device 200 can determine based on GNSS data that the computingdevice 200 is at a location in which POIs are aggregated (such as a mallwith multiple shops, restaurants and a movie theater). The movement ofthe computing device 200 can be tracked between the multiple POIs. Thecontext analyzer 212 can identify a label to be assigned to asignificant location based on the movement (or lack of movement) of thecomputing device 200 between the multiple POIs. For example, a dwelltime of the computing device 200 when the computing device 200 is at themovie theater is likely to be significantly higher than a dwell time ofthe computing device 200 when the computing device 200 is elsewhere(such as at a store or a restaurant). Based on the increased dwell time,the context analyzer 212 can determine that the computing device 200 isat the movie theater. The context analyzer 212 can identify a label forthe movie theater based on the movie theater having also been designatedas a POI, and store the label in movement data 312.

In some implementations, the computing device 200 can include one ormore motion sensors (e.g., accelerometer, gyroscope, magnetometer orother motion sensors). Using the motion sensors or other sensors (orboth), the context analyzer 212 can determine a type of physicalactivity being performed by the user of the computing device 200. Forexample, if the motion sensor indicates oscillation, the speed indicatesrunning and the location of the computing device 200 remains unchanged,then the context analyzer 212 can determine that the user is running ona treadmill. If the context analyzer 212 determines that the potentialsignificant locations at which the computing device 200 can be presentcan include a gym or a travel agency, the context analyzer 212 canincrease a likelihood that the computing device 200 is at the gym, notthe travel agency, based on motion information sensed by the motionsensors or other sensors.

Other Examples

In some implementations, computing device 200 can determine a label fora significant location based on other data 314. The contextual datasources 214 can include other data 312 storing labels assigned tosignificant locations using techniques other than those described here.For example, the computing device 200 can determine that a wirelessnetwork signature received by the computing device 200 is identical to awireless network signature of another computing device 200 indicating astrong likelihood that both computing devices are at the same geographiclocation. Also, both computing devices can be connected to the samecentral server that has determined (or received information specifying)a label for the geographic location in which the other computing deviceis located. Because both computing devices are located at the samegeographic location, the computing device 200 can receive the label fromthe central server and assign the label to the geographic location atwhich the computing device 200 is located. As an alternative or inaddition to receiving the label from the same central server, thecomputing device 200 can receive the label from the other computingdevice.

In another example, the computing device 200 can be located next to twoPOIs, each having an equal or nearly equal likelihood of being asignificant location. The context analyzer 212 can determine that one ofthe two POIs is closed at a time at which the computing device 200 islocated next to the two POIs. Based on this determination, the contextanalyzer 212 can determine that the POI that is open is the significantlocation and assign the label of the open POI to the significantlocation.

In a further example, each context data source can assign a label to asignificant location after multiple visits by the user to the samesignificant location.

Each of the context data sources described above can provide contextualdata points for identifying a label to be associated with a significantlocation. The context analyzer 212 can identify multiple potentialsignificant locations based on the contextual data points. The contextanalyzer 212 can disambiguate and resolve the multiple contextual datapoints to identify one of the multiple potential significant locationsas having the highest likelihood of being the significant location atwhich the computing device 200 is located. For example, the contextanalyzer 212 can resolve the multiple potential significant locations toone significant location if more than half of the contextual data pointsidentify the same POI as being that significant location. In anotherexample, the context analyzer 212 can assign a respective weight to eachpotential significant location. The weights can be the same ordifferent. For example, a contextual data point received from a mapsapplication can be given more weight than a contextual data pointreceived from a text messaging application. To resolve the multiplepotential significant locations to one significant location, the contextanalyzer 212 can execute a weighted algorithm that considers therespective weight given to each contextual data point.

In some implementations, a search engine history, favorites list,reading list or bookmarks may contain contextual data that can be usedto label a significant location. If multiple POIs are near a significantlocation, then the user's search history, favorites list, reading listor bookmarks can be used to filter the POIs. For example, referringagain to the example of FIG. 1 , a significant location may be nearMOVIE THEATER and BURGER PLACE. Each of these POIs could be the actualsignificant location. However, the user bookmarked BURGER PLACE and/orhas a search history that includes multiple searches on BURGER PLACE. Inthis case, BURGER PLACE could be selected over MOVIE THEATER as beingthe significant location.

In an embodiment, voice commands can be parsed to extract words orphrases that could provide “hints” about which of multiple POIs near asignificant location is the significant location. For example anintelligent personal assistant or knowledge navigator could receive avoice command “navigate to Burger Place” or a voice command “call BurgerPlace”. These voice commands can be contextual data that can be used tofilter POIs.

FIG. 3B is a schematic diagram illustrating an extension of a geographicarea of uncertainty encompassing a significant location. In the examplesdescribed above, data stored in each context data source was describedas identifying a label for one significant location. In the exampleshown, an estimated significant location 350 is surrounded by ageographic area of uncertainty 352. The geographic area of uncertainty352 represents an uncertainty associated with the estimated significantlocation 350. That is, the actual significant location computing device200 can be located anywhere within the geographic area of uncertainty352 represented by the uncertainty. To label the significant location,the computing device 200 can consider all the POIs that reside (eithercompletely or partially) within the geographic area of uncertainty 352.In some implementations, to label the significant location, thecomputing device 200 can consider all the POIs that reside (eithercompletely or partially) within a second geographic area of uncertainty354 that is larger than (for example, by a factor greater than one suchas two) the geographic area of uncertainty 352.

FIG. 4 is a flowchart of an example of a process 400 for identifyinglabels for significant locations. The process 400 can be implemented ascomputer instructions stored on a computer-readable storage medium andexecuted by one or more processors. For example, the process 400 can beimplemented by the device architecture 500 described in reference toFIG. 5 . In general, process 400 determines a significant location ofthe computing device. For example, at 402, process 400 obtains one ormore significant locations. For example, a significant locationestimator can provide a list of significant locations. At 404, for eachsignificant location, process 400 identifies one or more POIs near thesignificant location. Each POI represents an entity, such as a business,public landmark, school, hospital, etc. Process 400 determines a labelfor the determined significant location based on contextual dataassociated with the significant location. At 406, process 400 determinesthat a POI is the significant location based on contextual data.Examples of contextual data include but are not limited to: transactiondata, calendar data, map data, communication data, voice commands, andany other data that can provide information that can be used to identifya POI as a significant location. At 408, a label associated with thedetermined POI is used to label the significant location.

Exemplary Determination of Significant Locations

Some examples of techniques to determine significant locations aredescribed in this and the following paragraphs. Details aboutdetermining significant locations are described in U.S. patentapplication Ser. No. 14/502,677 entitled “Location-Based Services ForCalendar Events”, filed on Sep. 30, 2014 and the entire contents ofwhich are incorporated by reference herein in its entirety. Computingdevice 200 can use machine learning and data mining techniques to learnthe past movement of computing device 200. The past movement can berecorded as significant locations visited by computing device 200 andmovement of computing device 200 between the significant locations.Computing device 200 can determine that a place or region is asignificant location upon determining that, with sufficient certainty,computing device 200 has stayed at the place or region for a sufficientamount of time. The amount of time can be sufficient if it satisfiesvarious criteria, for example, when the amount of time satisfies a timelength threshold (for example, X hours) or a frequency threshold (forexample, X minutes per day, Y number of days per week). Records ofmovement of computing device 200 can include a measured or calculatedtime of entry into each significant location and a measured orcalculated time of exit from each significant location. A significantlocation can be associated with multiple entries and exits.

In addition to significant locations, the records of movement caninclude transitions between the significant locations. Each transitionfrom a first significant location to a second significant location canbe associated with a transition begin timestamp indicating a timecomputing device 200 leaves the first significant location and atransition end timestamp indicating a time computing device 200 entersthe second significant location.

Computing device 200 can represent the records of movement as a statemodel. State model can include states, each representing a significantlocation, and transitions, each representing a movement of computingdevice 200 between significant locations. Additional details ofdetermining the state model are described below.

Based on the state model, computing device 200 can determine (1) atransition probability density that, at a given time, computing device200 moves from a given significant location to each other significantlocation, or (2) an entry probability density that computing device 200enters a significant location from a previously unknown or unrepresentedlocation. A pattern analyzer of computing device 200 can determine adaily, weekly, monthly, or annual movement pattern of computing device200 using the state model. A predictive engine of computing device 200can use transition probability density (or entry probability density)and the movement pattern to forecast a significant location thatcomputing device 200 will enter (or stay) at a future time. Computingdevice 200 can then use the forecast to provide predictive userassistance, for example, to assist the user to plan for a future event.

Exemplary Techniques of Constructing a State Model

The computing device 200 can use the learning techniques to determinethe state model. Computing device 200 can sequentially trace locationdata through time (T). Sequentially tracing location data can beperformed by piggybacking on another application to avoid or reduce costof location data collection. For example, computing device 200 cancollect the location data when another service requests location from alocation determination subsystem of computing device 200. Accordingly,collecting the location data can be “free” without having to activatethe location determination subsystem solely for determining a movementpattern of computing device 200.

Computing device 200 can collect multiple locations over time T.Collecting new locations can be ongoing operations. Locations can bepurged based on a variety of policies, including age, user preference orprivacy. The multiple locations can each include latitude, longitude,and altitude coordinates, with a degree of uncertainty in each of these,and be associated with a timestamp indicating a time the correspondinglocation is collected.

Computing device 200 can determine that some of locations belong tolocation clusters that may indicate a significant location. Computingdevice 200 can determine that a location cluster is formed upondetermining that (1) at least a pre-specified threshold number (forexample, two) of consecutive locations are collected; (2) a time span ofthe consecutive locations satisfies a pre-specified threshold timewindow; and (3) these locations are identical, indicating that computingdevice 200 is stationary, or sufficiently close to one another,indicating that computing device 200 is located in a sufficiently smalland defined area during the time the locations are collected.

For example, computing device 200 can determine two location clustersover time T. A first location cluster can include a first subset oflocations collected over a time period [T1, T2] that is longer than athreshold time window (for example, a time window of 45 minutes).Computing device 200 can determine that the first location clusterincludes the locations in the subset upon determining that a variance ofthe locations is low enough to satisfy a variance threshold. Likewise, asecond location cluster can include a second subset of locations, whichare collected within time period [T3, T4]. Computing device 200 candetermine that the second location cluster includes the locations in thesecond subset upon determining that a variance of the locationssatisfies the variance threshold.

An outlier detection mechanism can filter out locations that do notbelong to clusters. For example, computing device 200 can determine thata location is different from locations in the two subsets (for example,based on a distance threshold being exceeded). In addition, computingdevice 200 can determine that no other locations are (1) collectedwithin the threshold time window before or after the location and (2)geographically close to that location. In response, computing device 200can determine that the location is an outlier and discard the location.In addition, if a location in a time period is significantly differentfrom many other locations in the time period, computing device 200 candiscard the different location as an outlier and determine the locationcluster using other locations in the time window. Computing device 200can use the first and second location clusters to determine significantlocations and states of the state model.

In some implementations, one of the conditions for determining alocation cluster is that a time span of the consecutive locationssatisfies a variable threshold time window. The threshold can vary basedon whether computing device 200 has a hint of significance of alocation.

At various times, computing device 200 can be located at differentlocations. The different locations can be far apart from one another,indicating that computing device 200 is moving. Computing device 200 canbe located at the different locations during a continuous period oftime. The different locations can be identical or sufficiently close toone another. Computing device 200 can determine whether the period oftime is sufficiently long such that the different locations form alocation cluster that indicates a significant location, based on whetherthe period of time satisfies a variable threshold. Computing device 200can use various hints to determine the variable threshold.

For example, computing device 200 can search locations where computingdevice 200 visited previously. Computing device 200 can designate as afirst hint a record indicating that computing device 200 previouslyvisited the location at or near the different locations as a first hint.Computing device 200 can examine a user search history performed on orthrough computing device 200. If the user searched for the locationbefore, computing device 200 can designate a search query including anaddress at or near the different locations, or a business located at ornear the different locations, as a second hint. Computing device 200 candesignate a calendar item in a user calendar (for example, anappointment or a meeting) located at or near the different locations asa third hint.

Upon detecting one or more hints, computing device 200 can use a shortertime period, for example, five minutes, as a threshold for determining alocation cluster or significant location. More hints can correspond toshorter threshold. Accordingly, computing device 200 can determine asignificant location upon detecting a location of the computing device,when the short time threshold is satisfied.

If no hint is found, computing device 200 can use a longer time period,for example, 20 minutes, as a threshold for determining a locationcluster or significant location. Accordingly, when no hint is found,computing device 200 can determine a location cluster or significantlocation upon detecting a location of computing device 200, when thelong time threshold is satisfied. In either case, with or without ahint, computing device 200 can determine a significant location in realtime, for example, 5 minutes or 20 minutes after locations converge intoa cluster.

Using the techniques described above, computing device 200 can identifylocation clusters. Computing device 200 can determine significantlocations based on the location clusters.

Computing device 200 can determine each of the significant locationsbased on the location clusters using the locations in each of thelocation clusters. Determining the significant locations can be based onrecursive filter with a constant gain. Details of determining thesignificant locations are provided below. Each of the significantlocations can include latitude, longitude, and optionally, altitudecoordinates. Each of the significant locations can be associated withone or more location clusters. For example, a first significant locationcan correspond to a first location cluster in time period [T1, T2] and athird location cluster during time period [T7, T8]. The location in thefirst location cluster and the third location cluster can be identical.The length of time period [T1, T2] and time window [T7, T8] can be thesame or different.

Computing device 200 can have an initial state model at time T2. At timeT2+k, computing device 200 can receive incremental location data, wherek is a difference between time T2 and the time the additional locationdata are received (in this example, k=T7−T2). Computing device 200 canuse the incremental location data to determine the significant locationfor use in the state model. Computing device 200 can determine that thefirst location cluster corresponds to latitude and longitude coordinatesX1. Computing device 200 can determine that the third location clustercorresponds to latitude and longitude coordinates X2. Computing device200 can determine that a distance between X1 and X2 satisfies athreshold. In response, computing device 200 can determine that thefirst location cluster and the third location cluster belong to a samelocation (i.e., the same significant location). Computing device 200 canthen add the third location cluster to the significant location usingconstant gain filter as shown below in Equation (1).

$\begin{matrix}{\frac{{X2} + {\alpha X1}}{1 + \alpha},{{{where}\alpha} \geq 1}} & (1)\end{matrix}$

Each of the significant locations can be associated with one or moreentry timestamps and one or more exit timestamps. Each entry timestampcan correspond to a time associated with a first location in a locationcluster. For example, a first entry timestamp associated with thesignificant location can be a timestamp associated with a location,which is the first location of the first location cluster. A secondentry timestamp associated with a significant location can be atimestamp associated with a first location in the third locationcluster. Likewise, each exit timestamp can correspond to a timeassociated with a last location in a location cluster. For example, afirst exit timestamp associated with the first significant location canbe a timestamp associated with the location, which is the last locationof the first location cluster. A second entry timestamp associated withthe significant location can be a timestamp associated with a lastlocation in the third location cluster.

Exemplary Device Architecture

FIG. 5 is a block diagram of example device architecture forimplementing the features and processes described in reference to FIGS.1-4 . Architecture 500 may be implemented in any computing device forgenerating the features and processes described in reference to FIGS.1-4 , including but not limited to smart phones and wearable computers(e.g., smart watches, fitness bands). Architecture 500 may includememory interface 502, data processor(s), image processor(s) or centralprocessing unit(s) 504, and peripherals interface 506. Memory interface502, processor(s) 504 or peripherals interface 506 may be separatecomponents or may be integrated in one or more integrated circuits. Oneor more communication buses or signal lines may couple the variouscomponents.

Sensors, devices, and subsystems may be coupled to peripherals interface506 to facilitate multiple functionalities. For example, motion sensor510, light sensor 512, and proximity sensor 514 may be coupled toperipherals interface 506 to facilitate orientation, lighting, andproximity functions of the device. For example, in some implementations,light sensor 512 may be utilized to facilitate adjusting the brightnessof touch surface 546. In some implementations, motion sensor 510 (e.g.,an accelerometer, rate gyroscope) may be utilized to detect movement andorientation of the device. Accordingly, display objects or media may bepresented according to a detected orientation (e.g., portrait orlandscape).

Other sensors may also be connected to peripherals interface 506, suchas a temperature sensor, a barometer, a biometric sensor, or othersensing device, to facilitate related functionalities. For example, abiometric sensor can detect fingerprints and monitor heart rate andother fitness parameters.

Location processor 515 (e.g., GNSS receiver chip) may be connected toperipherals interface 506 to provide geo-referencing. Electronicmagnetometer 516 (e.g., an integrated circuit chip) may also beconnected to peripherals interface 506 to provide data that may be usedto determine the direction of magnetic North. Thus, electronicmagnetometer 516 may be used as an electronic compass.

Camera subsystem 520 and an optical sensor 522, e.g., a charged coupleddevice (CCD) or a complementary metal-oxide semiconductor (CMOS) opticalsensor, may be utilized to facilitate camera functions, such asrecording photographs and video clips.

Communication functions may be facilitated through one or morecommunication subsystems 524. Communication subsystem(s) 524 may includeone or more wireless communication subsystems. Wireless communicationsubsystems 524 may include radio frequency receivers and transmittersand/or optical (e.g., infrared) receivers and transmitters. Wiredcommunication systems may include a port device, e.g., a UniversalSerial Bus (USB) port or some other wired port connection that may beused to establish a wired connection to other computing devices, such asother communication devices, network access devices, a personalcomputer, a printer, a display screen, or other processing devicescapable of receiving or transmitting data.

The specific design and implementation of the communication subsystem524 may depend on the communication network(s) or medium(s) over whichthe device is intended to operate. For example, a device may includewireless communication subsystems designed to operate over a globalsystem for mobile communications (GSM) network, a GPRS network, anenhanced data GSM environment (EDGE) network, IEEE802.xx communicationnetworks (e.g., Wi-Fi, Wi-Max, ZigBee™), 3G, 4G, 4G LTE, code divisionmultiple access (CDMA) networks, near field communication (NFC), Wi-FiDirect and a Bluetooth™ network. Wireless communication subsystems 524may include hosting protocols such that the device may be configured asa base station for other wireless devices. As another example, thecommunication subsystems may allow the device to synchronize with a hostdevice using one or more protocols or communication technologies, suchas, for example, TCP/IP protocol, HTTP protocol, UDP protocol, ICMPprotocol, POP protocol, FTP protocol, IMAP protocol, DCOM protocol, DDEprotocol, SOAP protocol, HTTP Live Streaming, MPEG Dash and any otherknown communication protocol or technology.

Audio subsystem 526 may be coupled to a speaker 528 and one or moremicrophones 530 to facilitate voice-enabled functions, such as voicerecognition, voice replication, digital recording, and telephonyfunctions.

I/O subsystem 540 may include touch controller 542 and/or other inputcontroller(s) 544. Touch controller 542 may be coupled to a touchsurface 546. Touch surface 546 and touch controller 542 may, forexample, detect contact and movement or break thereof using any of anumber of touch sensitivity technologies, including but not limited to,capacitive, resistive, infrared, and surface acoustic wave technologies,as well as other proximity sensor arrays or other elements fordetermining one or more points of contact with touch surface 546. In oneimplementation, touch surface 546 may display virtual or soft buttonsand a virtual keyboard, which may be used as an input/output device bythe user.

Other input controller(s) 544 may be coupled to other input/controldevices 548, such as one or more buttons, rocker switches, thumb-wheel,infrared port, USB port, and/or a pointer device such as a stylus. Theone or more buttons (not shown) may include an up/down button for volumecontrol of speaker 528 and/or microphone 530.

In some implementations, device 500 may present recorded audio and/orvideo files, such as MP3, AAC, and MPEG video files. In someimplementations, device 500 may include the functionality of an MP3player and may include a pin connector for tethering to other devices.Other input/output and control devices may be used.

Memory interface 502 may be coupled to memory 550. Memory 550 mayinclude high-speed random access memory or non-volatile memory, such asone or more magnetic disk storage devices, one or more optical storagedevices, or flash memory (e.g., NAND, NOR). Memory 550 may storeoperating system 552, such as Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks. Operatingsystem 552 may include instructions for handling basic system servicesand for performing hardware dependent tasks. In some implementations,operating system 552 may include a kernel (e.g., UNIX kernel).

Memory 550 may also store communication instructions 554 to facilitatecommunicating with one or more additional devices, one or more computersor servers, including peer-to-peer communications. Communicationinstructions 554 may also be used to select an operational mode orcommunication medium for use by the device, based on a geographiclocation (obtained by the GPS/Navigation instructions 568) of thedevice.

Memory 550 may include graphical user interface instructions 556 tofacilitate graphic user interface processing, including a touch modelfor interpreting touch inputs and gestures; sensor processinginstructions 558 to facilitate sensor-related processing and functions;phone instructions 560 to facilitate phone-related processes andfunctions; electronic messaging instructions 562 to facilitateelectronic-messaging related processes and functions; web browsinginstructions 564 to facilitate web browsing-related processes andfunctions; media processing instructions 566 to facilitate mediaprocessing-related processes and functions; GPS/Navigation instructions568 to facilitate GPS and navigation-related processes and functions;camera instructions 570 to facilitate camera-related processes andfunctions; and other instructions 572 for implementing the features andprocesses, as described in reference to FIGS. 1-4 .

Each of the above identified instructions and applications maycorrespond to a set of instructions for performing one or more functionsdescribed above. These instructions need not be implemented as separatesoftware programs, procedures, or modules. Memory 550 may includeadditional instructions or fewer instructions. Furthermore, variousfunctions of the device may be implemented in hardware and/or insoftware, including in one or more signal processing and/or applicationspecific integrated circuits (ASICs).

The features described may be implemented in digital electroniccircuitry or in computer hardware, firmware, software, or incombinations of them. The features may be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device, for execution by a programmableprocessor; and method steps may be performed by a programmable processorexecuting a program of instructions to perform functions of thedescribed implementations by operating on input data and generatingoutput.

The described features may be implemented advantageously in one or morecomputer programs that are executable on a programmable system includingat least one programmable processor coupled to receive data andinstructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that may be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program may be written in anyform of programming language (e.g., Objective-C, Java), includingcompiled or interpreted languages, and it may be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors orcores, of any kind of computer. Generally, a processor will receiveinstructions and data from a read-only memory or a random access memoryor both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Generally, a computer may communicate with mass storagedevices for storing data files. These mass storage devices may includemagnetic disks, such as internal hard disks and removable disks;magneto-optical disks; and optical disks. Storage devices suitable fortangibly embodying computer program instructions and data include allforms of non-volatile memory, including by way of example, semiconductormemory devices, such as EPROM, EEPROM, and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor andthe memory may be supplemented by, or incorporated in, ASICs(application-specific integrated circuits). To provide for interactionwith a user the features may be implemented on a computer having adisplay device such as a CRT (cathode ray tube), LED (light emittingdiode) or LCD (liquid crystal display) display or monitor for displayinginformation to the author, a keyboard and a pointing device, such as amouse or a trackball by which the author may provide input to thecomputer.

One or more features or steps of the disclosed embodiments may beimplemented using an Application Programming Interface (API). An API maydefine one or more parameters that are passed between a callingapplication and other software code (e.g., an operating system, libraryroutine, function) that provides a service, that provides data, or thatperforms an operation or a computation. The API may be implemented asone or more calls in program code that send or receive one or moreparameters through a parameter list or other structure based on a callconvention defined in an API specification document. A parameter may bea constant, a key, a data structure, an object, an object class, avariable, a data type, a pointer, an array, a list, or another call. APIcalls and parameters may be implemented in any programming language. Theprogramming language may define the vocabulary and calling conventionthat a programmer will employ to access functions supporting the API. Insome implementations, an API call may report to an application thecapabilities of a device running the application, such as inputcapability, output capability, processing capability, power capability,communications capability, etc.

As described above, some aspects of the subject matter of thisspecification include gathering and use of data available from varioussources to improve services a computing device can provide to a user.The present disclosure contemplates that in some instances, thisgathered data may identify a particular location or an address based ondevice usage. Such personal information data can include location baseddata, addresses, subscriber account identifiers, or other identifyinginformation.

The present disclosure further contemplates that the entitiesresponsible for the collection, analysis, disclosure, transfer, storage,or other use of such personal information data will comply withwell-established privacy policies and/or privacy practices. Inparticular, such entities should implement and consistently use privacypolicies and practices that are generally recognized as meeting orexceeding industry or governmental requirements for maintaining personalinformation data private and secure. For example, personal informationfrom users should be collected for legitimate and reasonable uses of theentity and not shared or sold outside of those legitimate uses. Further,such collection should occur only after receiving the informed consentof the users. Additionally, such entities would take any needed stepsfor safeguarding and securing access to such personal information dataand ensuring that others with access to the personal information dataadhere to their privacy policies and procedures. Further, such entitiescan subject themselves to evaluation by third parties to certify theiradherence to widely accepted privacy policies and practices.

In the case of advertisement delivery services, the present disclosurealso contemplates embodiments in which users selectively block the useof, or access to, personal information data. That is, the presentdisclosure contemplates that hardware and/or software elements can beprovided to prevent or block access to such personal information data.For example, in the case of advertisement delivery services, the presenttechnology can be configured to allow users to select to “opt in” or“opt out” of participation in the collection of personal informationdata during registration for services.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, content can beselected and delivered to users by inferring preferences based onnon-personal information data or a bare minimum amount of personalinformation, such as the content being requested by the deviceassociated with a user, other non-personal information available to thecontent delivery services, or publicly available information.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made. Elements of one ormore implementations may be combined, deleted, modified, or supplementedto form further implementations. In yet another example, the logic flowsdepicted in the figures do not require the particular order shown, orsequential order, to achieve desirable results. In addition, other stepsmay be provided, or steps may be eliminated, from the described flows,and other components may be added to, or removed from, the describedsystems. Accordingly, other implementations are within the scope of thefollowing claims.

What is claimed is:
 1. A method implemented by a computing device, themethod comprising: receiving, from a location subsystem, locationinformation at a geographic location for the computing device;determining, by the computing device, that the geographic location ofthe computing device is associated with a significant location in a setof significant locations accessible on the computing device, wherein thesignificant location is a location having position coordinates inferredfrom clusters of position coordinates previously gathered by thecomputing device in response to a determination that the computingdevice dwelled at the location for a threshold amount of time;determining, by the computing device, a label for the significantlocation based on analysis of contextual data for the significantlocation, the contextual data comprising a movement pattern for thecomputing device; and displaying, on a graphical interface of thecomputing device, the label for the significant location.
 2. The methodof claim 1, wherein the movement pattern of the computing devicecomprises movement of the computing device between a plurality ofsignificant locations, and wherein determining, by the computing device,the label for the determined significant location based on contextualdata associated with the significant location comprises determining thatthe computing device has a dwell time at the determined significantlocation that is greater than a threshold dwell time.
 3. The method ofclaim 1, further comprising: determining, based on the movement pattern,a physical activity performed by a user of the computing device at thesignificant location, wherein determining, by the computing device, thelabel for the determined significant location based on contextual dataassociated with the significant location comprises determining the labelbased on the physical activity.
 4. The method of claim 1, furthercomprising: determining, by a pattern analyzer of the computing device,at least one of a daily, weekly, a monthly, or an annual movementpattern of computing device using sensor data of the computing deviceand a state model.
 5. The method of claim 1, further comprising:receiving, by the location subsystem on the computing device, from anapplication executed by the computing device, a request for dataassociated with the significant location; and in response to therequest, providing the data associated with the significant location,wherein the data associated with the significant location includes thelabel for the significant location; and displaying, by a map engineassociated with the location subsystem, on behalf of the application andon the graphical interface displayed on the computing device, the labelfor the significant location at a location on the graphical interface.6. The method of claim 1, wherein determining the significant locationof the computing device comprises: obtaining, by the computing device,location information associated with the computing device; andreceiving, by the computing device, a plurality of points of interestassociated with the location information, wherein each point of interestrepresents an entity at a location near the geographic location of thecomputing device, the geographic location included in the locationinformation.
 7. The method of claim 6, wherein determining the label forthe determined significant location based on contextual data associatedwith the significant location comprises: determining, by the computingdevice and based on the contextual data associated with the points ofinterests, that one of the plurality of points of interest is thedetermined significant location; and determining a label associated withthe one of the plurality of points of interest as the label for thedetermined significant location.
 8. A data processing system on acomputing device, the system comprising: a memory device; one or moreprocessors to execute instructions stored on the memory device, whereinthe instructions cause the one or more processors to: receive, from alocation subsystem, location information at a geographic location forthe computing device; determine, by the computing device, that thegeographic location of the computing device is associated with asignificant location in a set of significant locations accessible on thecomputing device, wherein the significant location is a location havingposition coordinates inferred from clusters of position coordinatespreviously gathered by the computing device in response to adetermination that the computing device dwelled at the location for athreshold amount of time; determine, by the computing device, a labelfor the significant location based on analysis of contextual data forthe significant location, the contextual data comprising a movementpattern for the computing device; and display, on a graphical interfaceof the computing device, the label for the significant location.
 9. Thedata processing system of claim 8, wherein the movement pattern of thecomputing device comprises movement of the computing device between aplurality of significant locations, and wherein to determine, by thecomputing device, the label for the determined significant locationbased on contextual data associated with the significant locationcomprises determining that the computing device has a dwell time at thedetermined significant location that is greater than a threshold dwelltime.
 10. The data processing system of claim 8, wherein theinstructions further cause the one or more processors to: determine,based on the movement pattern, a physical activity performed by a userof the computing device at the significant location, whereindetermining, by the computing device, the label for the determinedsignificant location based on contextual data associated with thesignificant location comprises determining the label based on thephysical activity.
 11. The data processing system of claim 8, whereinthe instructions further cause the one or more processors to: determine,by a pattern analyzer of the computing device, at least one of a daily,weekly, a monthly, or an annual movement pattern of computing deviceusing sensor data of the computing device and a state model.
 12. Thedata processing system of claim 8, wherein the instructions furthercause the one or more processors to: receive, by the location subsystemon the computing device, from an application executed by the computingdevice, a request for data associated with the significant location; andin response to the request, provide the data associated with thesignificant location, wherein the data associated with the significantlocation includes the label for the significant location; and display,by a map engine associated with the location subsystem, on behalf of theapplication and on the graphical interface displayed on the computingdevice, the label for the significant location at a location on thegraphical interface.
 13. The data processing system of claim 8, whereinthe instructions further cause the one or more processors to: obtain, bythe computing device, location information associated with the computingdevice; and receive, by the computing device, a plurality of points ofinterest associated with the location information, wherein each point ofinterest represents an entity at a location near the geographic locationof the computing device, the geographic location included in thelocation information.
 14. The data processing system of claim 13,wherein to determine the label for the determined significant locationbased on contextual data associated with the significant locationcomprises: determining, by the computing device and based on thecontextual data associated with the points of interests, that one of theplurality of points of interest is the determined significant location;and determining a label associated with the one of the plurality ofpoints of interest as the label for the determined significant location.15. A non-transitory machine-readable medium storing instructions which,when executed by one or more processors of a computing device, cause theone or more processors to perform operations comprising: receiving, froma location subsystem, location information at a geographic location forthe computing device; determining, by the computing device, that thegeographic location of the computing device is associated with asignificant location in a set of significant locations accessible on thecomputing device, wherein the significant location is a location havingposition coordinates inferred from clusters of position coordinatespreviously gathered by the computing device in response to adetermination that the computing device dwelled at the location for athreshold amount of time; determining, by the computing device, a labelfor the significant location based on analysis of contextual data forthe significant location, the contextual data comprising a movementpattern for the computing device; and displaying, on a graphicalinterface of the computing device, the label for the significantlocation.
 16. The non-transitory machine-readable medium of claim 15,wherein the movement pattern of the computing device comprises movementof the computing device between a plurality of significant locations,and wherein determining, by the computing device, the label for thedetermined significant location based on contextual data associated withthe significant location comprises determining that the computing devicehas a dwell time at the determined significant location that is greaterthan a threshold dwell time.
 17. The non-transitory machine-readablemedium of claim 15, the operations further comprising: determining,based on the movement pattern, a physical activity performed by a userof the computing device at the significant location, whereindetermining, by the computing device, the label for the determinedsignificant location based on contextual data associated with thesignificant location comprises determining the label based on thephysical activity.
 18. The non-transitory machine-readable medium ofclaim 15, the operations further comprising: determining, by a patternanalyzer of the computing device, at least one of a daily, weekly, amonthly, or an annual movement pattern of computing device using sensordata of the computing device and a state model.
 19. The non-transitorymachine-readable medium of claim 15, the operations further comprising:receiving, by the location subsystem on the computing device, from anapplication executed by the computing device, a request for dataassociated with the significant location; and in response to therequest, providing the data associated with the significant location,wherein the data associated with the significant location includes thelabel for the significant location; and displaying, by a map engineassociated with the location subsystem, on behalf of the application andon a graphical interface displayed on the computing device, the labelfor the significant location at a location on the graphical interface.20. The non-transitory machine-readable medium of claim 15, theoperations further comprising: obtaining, by the computing device,location information associated with the computing device; andreceiving, by the computing device, a plurality of points of interestassociated with the location information, wherein each point of interestrepresents an entity at a location near the geographic location of thecomputing device, the geographic location included in the locationinformation.